Analysis of effect of alloying elements on martensite start temperature of steels

Abstract

Making the transformation from austenite to martensite difficult is called stabilisation of austenite, a phenomenon that occurs in many cases. The straightforward method to analyse the influence of a specific factor on the stabilisation of austenite is through its influence on the martensite start (M s ) temperature. This work outlines the use of an artificial neural network to model the M s temperature of engineering steels from their chemical composition and austenite grain size. The results are focussed on analysing the role in the stabilisation of austenite of alloying elements in steels including less common elements such as V and Nb, as well as the austenite grain size. Moreover, a physical interpretation of the results is presented.

Time-Temperature-Transformation Diagram within the Bainitic Temperature Range in a Medium Carbon Steel

“…However, in the left-hand side, upper bainite sheaves can be found. It can be then concluded that the transition from upper to lower bainite occurs at a temperature about 450 C. Table 2 lists the M s temperature of the studied steel calculated from different empirical formulas, 14,[18][19][20][21][22][23] thermodynamic theory, 24) and neural network analysis, [25][26][27] in comparison with the M s experimental value. The free energy change for the transformation of austenite to ferrite of the same composition, ÁG , input in Ghosh and Olson model 24) was calculated using MTDATA.…”

“…The inputs of Ghosh and Olson's model are the chemical composition and the free energy change for the transformation of austenite to ferrite of the same composition, ÁG . More recently, Capdevila et al [25][26][27] developed a model using artificial neural networks for the determination of M s , including the effect of chemical composition and the prior austenite grain size (PAGS) in calculations.…”

Effects of Morphology and Stability of Retained Austenite on the Ductility of TRIP-aided Bainitic Steels

“…8(a) confirm the high extent of carbon enrichment in austenite after bainite formation since the applicable chemistry range of the neural network model (1.62wt% C) seems to be exceed. 19) Since tensile tests were performed at room temperature, in principle, M S temperatures below 20°C are desired in order to increase the thermal stability of the retained austenite in the microstructure. Likewise, it is important to highlight what a high or low value of M d temperature implies in terms of mechanical stability and the efficiency enhancing ductility.…”

“…Both temperatures, M S and M d , give an indication of the thermal and mechanical stability, respectively, of the retained austenite and depend on the retained austenite chemical composition calculated making use of X-ray analysis. M S was calculated by means of a neural network model 19) and M d temperature was calculated using the following equation, 14) ln…”

“…Both temperatures, M S and M d , give an indication of the thermal and mechanical stability, respectively, of the retained austenite and depend on the retained austenite chemical composition calculated making use of X-ray analysis. M S was calculated by means of a neural network model 19) and M d temperature was calculated using the following equation, 14) lnwhere V 0 g and V g represent the initial austenite fraction and the remaining fraction, after transformation induced plasticity, respectively. Therefore M d corresponds to the temperature, T, at which V 0 g ϭV g ; k 1 is a constant, e is the plastic strain and DG aЈg (T) represents the chemical free energy change for the transformation of austenite to ferrite of the same composition determined using MTDATA with the NPL-plus data base for steels.…”